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Comparing Neural-Network Scoring Functions and the State of the Art: Applications to Common Library Screening
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Current Protocols in Bioinformatics
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Flexible Ligand Docking with Glide
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Ultrafast shape recognition to search compound databases for similar molecular shapes
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AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
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Virtual screening to enrich hit lists from high‐throughput screening: A case study on small‐molecule inhibitors of angiogenin
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A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance
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Binding MOAD (Mother Of All Databases)
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Folding funnels and conformational transitions via hinge-bending motions
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Binding site characteristics in structure-based virtual screening: evaluation of current docking tools
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How to do an evaluation: pitfalls and traps
inferredcites →https://doi.org/10.1007/s10822-007-9167-2openalex-snapshot
Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?
inferredcites →https://doi.org/10.1007/s10822-008-9169-8openalex-snapshot
Evaluating docking programs: keeping the playing field level
inferredcites →https://doi.org/10.1007/s10822-013-9644-8openalex-snapshot
Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments
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Signal detection theory and psychophysics
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A fluorescence-based high-throughput screening assay for inhibitors of human immunodeficiency virus-1 reverse transcriptase-associated ribonuclease H activity
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High-throughput screening assay for inhibitors of heat-shock protein 90 ATPase activity
inferredcites →https://doi.org/10.1016/j.bmc.2009.11.050openalex-snapshot
Novel and selective DNA methyltransferase inhibitors: Docking-based virtual screening and experimental evaluation
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High-Throughput Screening Identifies Inhibitors of the SARS Coronavirus Main Proteinase
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Computer-aided drug-discovery techniques that account for receptor flexibility
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Similarity-based virtual screening using 2D fingerprints
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Pharmacophore modeling and applications in drug discovery: challenges and recent advances
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Identification of novel selective antagonists for cyclin C by homology modeling and virtual screening
inferredcites →https://doi.org/10.1016/j.jmgm.2010.05.008openalex-snapshot
Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods
inferredcites →https://doi.org/10.1016/j.jmgm.2011.01.004openalex-snapshot
BINANA: A novel algorithm for ligand-binding characterization
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Statistical Methods for Research Workers
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High throughput screening identifies novel inhibitors of Escherichia coli dihydrofolate reductase that are competitive with dihydrofolate
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Protein flexibility and drug design: how to hit a moving target
inferredcites →https://doi.org/10.1021/ci0255026openalex-snapshot
A Novel Shape-Feature Based Approach to Virtual Library Screening
inferredcites →https://doi.org/10.1021/ci100062nopenalex-snapshot
Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments
inferredcites →https://doi.org/10.1021/ci100244vopenalex-snapshot
NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
inferredcites →https://doi.org/10.1021/ci2003889openalex-snapshot
NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
inferredcites →https://doi.org/10.1021/ci600426eopenalex-snapshot
Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem
inferredcites →https://doi.org/10.1021/ci700052xopenalex-snapshot
Comparison of Topological, Shape, and Docking Methods in Virtual Screening
inferredcites →https://doi.org/10.1021/ci900056copenalex-snapshot
Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy
inferredcites →https://doi.org/10.1021/ci900345hopenalex-snapshot
Ligand−Protein Cross-Docking with Water Molecules
inferredcites →https://doi.org/10.1021/jm0003992openalex-snapshot
Detailed Analysis of Scoring Functions for Virtual Screening
inferredcites →https://doi.org/10.1021/jm001044lopenalex-snapshot
Protein-Based Virtual Screening of Chemical Databases. 1. Evaluation of Different Docking/Scoring Combinations
inferredcites →https://doi.org/10.1021/jm010548wopenalex-snapshot
Molecular Docking and High-Throughput Screening for Novel Inhibitors of Protein Tyrosine Phosphatase-1B
inferredcites →https://doi.org/10.1021/jm030161oopenalex-snapshot
Multiple Active Site Corrections for Docking and Virtual Screening
inferredcites →https://doi.org/10.1021/jm0306430openalex-snapshot
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy
inferredcites →https://doi.org/10.1021/jm030644sopenalex-snapshot
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening
inferredcites →https://doi.org/10.1021/jm049798dopenalex-snapshot
Comparison of Automated Docking Programs as Virtual Screening Tools
inferredcites →https://doi.org/10.1021/jm050362nopenalex-snapshot
A Critical Assessment of Docking Programs and Scoring Functions
inferredcites →https://doi.org/10.1021/jm0608356openalex-snapshot
Benchmarking Sets for Molecular Docking
inferredcites →https://doi.org/10.1021/jm900776mopenalex-snapshot
Novel Inhibitors of Dengue Virus Methyltransferase: Discovery by in Vitro-Driven Virtual Screening on a Desktop Computer Grid
inferredcites →https://doi.org/10.1021/ml2001714openalex-snapshot
Discovery of Novel α4β2 Neuronal Nicotinic Receptor Modulators through Structure-Based Virtual Screening
inferredcites →https://doi.org/10.1038/nrd1129openalex-snapshot
Implications of protein flexibility for drug discovery
inferredcites →https://doi.org/10.1038/sj.bjp.0707515openalex-snapshot
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go
inferredcites →https://doi.org/10.1039/c0cp00151aopenalex-snapshot
Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions
inferredcites →https://doi.org/10.1073/pnas.44.2.98openalex-snapshot
Application of a Theory of Enzyme Specificity to Protein Synthesis
inferredcites →https://doi.org/10.1074/jbc.m501100200openalex-snapshot
High-throughput Screening for Potent and Selective Inhibitors of Plasmodium falciparum Dihydroorotate Dehydrogenase
inferredcites →https://doi.org/10.1093/ajcp/15.11.552bopenalex-snapshot
Statistical Methods for Research Workers
inferredcites →https://doi.org/10.1093/protein/12.9.713openalex-snapshot
Folding funnels and binding mechanisms
inferredcites →https://doi.org/10.1110/ps.21302openalex-snapshot
Multiple diverse ligands binding at a single protein site: A matter of pre‐existing populations
inferredcites →https://doi.org/10.1110/ps.8.6.1181openalex-snapshot
Folding funnels, binding funnels, and protein function
inferredcites →https://doi.org/10.1186/1758-2946-1-14openalex-snapshot
Optimal assignment methods for ligand-based virtual screening
inferredcites →https://doi.org/10.1504/ijcse.2009.029165openalex-snapshot
F2PY: a tool for connecting Fortran and Python programs
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Improvements, trends, and new ideas in molecular docking: 2012–2013 in review
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Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets
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Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
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From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
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Homology modeling and virtual screening studies of FGF-7 protein—a structure-based approach to design new molecules against tumor angiogenesis
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An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
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Modern Tools and Techniques in Computer-Aided Drug Design
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Methods and applications of machine learning in structure-based drug discovery
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Can docking scoring functions guarantee success in virtual screening?
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Comparative assessment of machine-learning scoring functions on PDBbind 2013
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Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands
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Boosting Docking-Based Virtual Screening with Deep Learning
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Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review
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Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach
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Improvement of Virtual Screening Results by Docking Data Feature Analysis
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An artificial neural network model to predict structure-based protein–protein free energy of binding from Rosetta-calculated properties
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Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
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CGDeepAff: Deep Learning-Based Approach for Protein-Ligand Binding Affinity Estimation Using CNN-GRU
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Peer Review #1 of "Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation (v0.1)"
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Peer Review #2 of "Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation (v0.1)"
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Peer Review #3 of "Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation (v0.1)"
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Peer Review #1 of "Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation (v0.2)"
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Peer Review #3 of "Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation (v0.2)"
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Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation
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